Probing the Causal Contribution of Reasoning to Third-Party Moral Judgment of Harm Transgressions
Bibliographic record
Abstract
Abstract: Recent work has supported the role of reasoning in third-party moral judgment of harm transgressions. In particular, reasoning may increase the weight of intention in moral judgment, especially following accidental harm, a situation that presumably requires judges to balance considerations about the outcome endured by a victim on the one hand and considerations about an agent’s intention to cause harm on the other hand. Three preregistered lab-based studies aimed to test the causal contribution of reasoning to moral judgment of harm transgressions using experimental manipulations borrowed from the reasoning literature: time pressure (Experiment 1), cognitive load (Experiment 2), and priming (Experiment 3). Participants ( N = 284) were presented with short fictitious scenarios in which the agent’s intention toward a potential victim (harmful or neutral intent) and the action’s outcome (victim’s injury or no harm) were manipulated. Participants then reported their moral judgment of the agent’s behavior (wrongness and deserved punishment) and their empathy toward the victim. We found that time pressure reduced judgment severity toward agents who had the intention to harm, but the reasoning manipulations overall did not impact judgment severity toward agents who harmed accidentally. We discuss why reasoning may sometimes influence how individuals account for intention in third-party moral judgment of harm transgressions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".